Abstract

The main contribution of this work is to exhibit an improved developed and evaluation of architectures with higher accuracy for prosthetic hand by deep learning with IMU using 9 degrees of freedom. Deep Learning has been gaining popularity due to its numerous implementations and continuous growing capabilities, including the prosthetics industry which has contributed by developing prosthetic minded by deep learning. Previously conducted research on prosthetic arm that utilizes deep learning with inertial measurement unit (IMU) produced promising results with only using single aspect of the device. This research expanded on that by utilizing accelerometer, gyroscope, and magnetometer. This research study was successfully developed various architectures that yielded promising results with improving on the previous work by achieving 99.2% accuracy.